From Non-Rigid to Rigid: Safe Acquisition of Rigid Communication Graphs under Limited Sensing
2026-07-11 • Robotics
Robotics
AI summaryⓘ
The authors study how groups of robots can keep a strong and stable communication network even when they can only sense nearby robots and the situation is constantly changing. They offer a way to build and maintain a communication graph that stays 'rigid' so the robots can work together without crashing, even if they start disconnected. Their method works for different types of robots and does not need exact global positions, using a leader-follower setup and simple optimization rules. The approach was tested both in computer simulations and real-life experiments, showing it can reliably handle limited sensing.
communication graph rigiditymulti-robot formation controlsensing rangetime-varying graphsdistributed optimizationleader-follower architecturesecond-order consensusheterogeneous robotscollision avoidance
Authors
Saharsh, Vedhas Talnikar, Pushpak Jagtap
Abstract
Communication graph rigidity is a fundamental requirement in many multi robot formation control approaches. However, ensuring and maintaining a rigid communication topology becomes challenging in practice due to limited sensing ranges and dynamic operating conditions. This paper provides a method for achieving an inter robot collision free, rigid time varying communication graph, where communication links are established or broken according to limited sensing ranges, without assuming an initial rigid graph. In addition, the proposed approach guarantees the realization of a rigid graph for heterogeneous nonlinear multi robot systems. A computationally lean, distributed quadratic optimization-based controller is developed for a leader follower architecture, acquiring rigidity based on hierarchical second-order consensus among robots. Follower agents do not require global absolute positions of any agent, including their own. The proposed method is validated through both simulations and hardware experiments in a motion-capture environment, demonstrating reliable performance under the limited sensing capabilities of individual robots.